Research Intelligence MCP

A production-structured Model Context Protocol (MCP) server for academic paper discovery, citation exploration, and research intelligence workflows.

Research Intelligence MCP provides a unified interface over multiple scientific knowledge sources while exposing standardized MCP tools that can be consumed by AI systems such as ChatGPT, Claude Desktop, Cursor, ResearchMind, and custom agents.

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Motivation

Modern AI systems increasingly require access to external research knowledge.

Academic information is fragmented across multiple providers:

• Semantic Scholar
• arXiv
• OpenAlex
• CrossRef
• PubMed
• IEEE
• Springer
• Nature

Every provider exposes different APIs, schemas, identifiers, and capabilities.

This project solves that problem by providing:

• Unified paper models
• Provider abstraction
• Search federation
• Citation graph exploration
• Open-access paper resolution
• MCP-compatible tooling

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Features

Current Version

Academic Search

• Search scientific papers
• Search recent arXiv publications
• Retrieve paper metadata
• Discover related papers
• Retrieve citations and references
• Resolve available open-access PDFs

Architecture

• Official MCP Python SDK
• Async Python architecture
• Provider abstraction layer
• Canonical domain models
• Structured logging
• Retry policies
• Caching support
• Rate limiting support
• Production-quality project structure

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Supported Providers

Phase 1

• Semantic Scholar
• arXiv

Planned Providers

• CrossRef
• OpenAlex
• Papers With Code
• PubMed
• IEEE
• Springer Nature

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Example Use Cases

Find recent papers

Find recent papers about Agentic RAG.

Discover related work

Find papers related to LangGraph multi-agent systems.

Citation exploration

What papers cite the original RAG paper?

Open-access resolution

Find the PDF for this research paper.

Research agent integration

ResearchMind
        ↓
Research Intelligence MCP
        ↓
Semantic Scholar
arXiv

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Architecture

┌──────────────────────┐
│      MCP Tools       │
└──────────┬───────────┘
           │
┌──────────▼───────────┐
│      Services        │
└──────────┬───────────┘
           │
┌──────────▼───────────┐
│ Provider Abstraction │
└──────────┬───────────┘
           │
 ┌─────────┴─────────┐
 │                   │
▼                     ▼
Semantic Scholar     arXiv

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Project Structure

research-intelligence-mcp/
├── src/
│   └── research_intelligence_mcp/
├── tests/
├── pyproject.toml
├── README.md
└── .env.example

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Requirements

• Python 3.12+
• uv
• Git

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Installation

Clone repository:

git clone <repository-url>
cd research-intelligence-mcp

Create virtual environment:

uv venv
source .venv/bin/activate

Install dependencies:

uv sync

Create environment file:

cp .env.example .env

---

Running

Run the MCP server:

uv run research-intelligence-mcp

or

python -m research_intelligence_mcp

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Quality Checks

Format:

uv run ruff format .

Lint:

uv run ruff check .
uv run ruff check . --fix

Type checking:

uv run mypy src

Tests:

uv run pytest

Package build verification:

uv build

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MCP Configuration Example

{
  "mcpServers": {
    "research-intelligence-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/research-intelligence-mcp",
        "run",
        "research-intelligence-mcp"
      ]
    }
  }
}

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Test using MCP Inspector

The official MCP Inspector is the recommended interactive tool for viewing registered MCP tools, their schemas, parameters, and execution results. project root, run:

npx @modelcontextprotocol/inspector \
  uv \
  --directory "$(pwd)" \
  run \
  research-intelligence-mcp

The Inspector should open in your browser.

Then: Connect to MCP Server Open the Tools tab. List tools Select health_check. Run the tool.

Expected structured result:

{
  "status": "healthy",
  "service": "Research Intelligence MCP",
  "server_name": "research-intelligence-mcp",
  "version": "0.1.0",
  "environment": "development",
  "transport": "stdio",
  "timestamp": "2026-07-20T..."
}

Startup flow

main()
  │
  ├── load settings
  ├── configure stderr logging
  ├── build dependency container
  ├── create FastMCP server
  ├── register tools
  └── run stdio transport

MCP Tools

Search Tool

License

MIT